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Extending the Cluster Approach to Differential Item Functioning in Polytomous Items

Author

Listed:
  • Martijn Schoenmakers

    (Tilburg University)

  • Jesper Tijmstra

    (Tilburg University)

  • Jeroen Vermunt

    (Tilburg University)

  • Maria Bolsinova

    (Tilburg University)

Abstract

To objectively compare groups on any latent trait using tests, the absence of differential item functioning (DIF) is crucial. While the importance of DIF has been well-established in research, the question of how to identify DIF-free items is still largely open. The fact that item difficulty is not identified from observations may explain this. Recently, DIF tests utilizing the differences between item difficulties across groups, which are identified, were proposed for the Rasch and 2-parameter logistic models. The current paper aims to extend these approaches to the polytomous case using the partial credit model. Performance of the new approach is assessed using a simulation study, and practical recommendations are made.

Suggested Citation

  • Martijn Schoenmakers & Jesper Tijmstra & Jeroen Vermunt & Maria Bolsinova, 2025. "Extending the Cluster Approach to Differential Item Functioning in Polytomous Items," Journal of Educational and Behavioral Statistics, , vol. 50(3), pages 471-496, June.
    • Handle: RePEc:sae:jedbes:v:50:y:2025:i:3:p:471-496
    DOI: 10.3102/10769986241256033
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    References listed on IDEAS

    as
    1. Geoff Masters, 1982. "A rasch model for partial credit scoring," Psychometrika, Springer;The Psychometric Society, vol. 47(2), pages 149-174, June.
    2. Timo Bechger & Gunter Maris, 2015. "A Statistical Test for Differential Item Pair Functioning," Psychometrika, Springer;The Psychometric Society, vol. 80(2), pages 317-340, June.
    3. Chalmers, R. Philip, 2012. "mirt: A Multidimensional Item Response Theory Package for the R Environment," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 48(i06).
    Full references (including those not matched with items on IDEAS)

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